memory_allocation.rst 3.3 KB

Memory Allocation

Dynamic memory allocation is mostly a non-issue in Python. Everything is an object, and the reference counting system and garbage collector automatically return memory to the system when it is no longer being used.

When it comes to more low-level data buffers, Cython has special support for (multi-dimensional) arrays of simple types via NumPy, memory views or Python's stdlib array type. They are full featured, garbage collected and much easier to work with than bare pointers in C, while still retaining the speed and static typing benefits. See :ref:`array-array` and :ref:`memoryviews`.

In some situations, however, these objects can still incur an unacceptable amount of overhead, which can then makes a case for doing manual memory management in C.

Simple C values and structs (such as a local variable cdef double x) are usually allocated on the stack and passed by value, but for larger and more complicated objects (e.g. a dynamically-sized list of doubles), the memory must be manually requested and released. C provides the functions :c:func:`malloc`, :c:func:`realloc`, and :c:func:`free` for this purpose, which can be imported in cython from clibc.stdlib. Their signatures are:

void* malloc(size_t size)
void* realloc(void* ptr, size_t size)
void free(void* ptr)

A very simple example of malloc usage is the following:

import random
from libc.stdlib cimport malloc, free

def random_noise(int number=1):
    cdef int i
    # allocate number * sizeof(double) bytes of memory
    cdef double *my_array = <double *>malloc(number * sizeof(double))
    if not my_array:
        raise MemoryError()

    try:
        ran = random.normalvariate
        for i in range(number):
            my_array[i] = ran(0,1)

        return [ my_array[i] for i in range(number) ]
    finally:
        # return the previously allocated memory to the system
        free(my_array)

Note that the C-API functions for allocating memory on the Python heap are generally preferred over the low-level C functions above as the memory they provide is actually accounted for in Python's internal memory management system. They also have special optimisations for smaller memory blocks, which speeds up their allocation by avoiding costly operating system calls.

The C-API functions can be found in the cpython.mem standard declarations file:

from cpython.mem cimport PyMem_Malloc, PyMem_Realloc, PyMem_Free

Their interface and usage is identical to that of the corresponding low-level C functions.

One important thing to remember is that blocks of memory obtained with :c:func:`malloc` or :c:func:`PyMem_Malloc` must be manually released with a corresponding call to :c:func:`free` or :c:func:`PyMem_Free` when they are no longer used (and must always use the matching type of free function). Otherwise, they won't be reclaimed until the python process exits. This is called a memory leak.

If a chunk of memory needs a larger lifetime than can be managed by a try..finally block, another helpful idiom is to tie its lifetime to a Python object to leverage the Python runtime's memory management, e.g.: